AIMC Topic: Diagnostic Imaging

Clear Filters Showing 21 to 30 of 978 articles

Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques.

Oncotarget
Recent advances in deep learning models have transformed medical imaging analysis, particularly in radiology. This editorial outlines how uncertainty quantification through embedding-based approaches enhances diagnostic accuracy and reliability in he...

Pyramid Pixel Context Adaption Network for Medical Image Classification With Supervised Contrastive Learning.

IEEE transactions on neural networks and learning systems
Spatial attention (SA) mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysi...

Label-Free Medical Image Quality Evaluation by Semantics-Aware Contrastive Learning in IoMT.

IEEE journal of biomedical and health informatics
With the rapid development of the Internet-of-Medical-Things (IoMT) in recent years, it has emerged as a promising solution to alleviate the workload of medical staff, particularly in the field of Medical Image Quality Assessment (MIQA). By deploying...

Explainable AI for Medical Image Analysis in Medical Cyber-Physical Systems: Enhancing Transparency and Trustworthiness of IoMT.

IEEE journal of biomedical and health informatics
This study explores the application of explainable artificial intelligence (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. Meanwhile, this study proposes an expl...

Adaptive Dual-Axis Style-Based Recalibration Network With Class-Wise Statistics Loss for Imbalanced Medical Image Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Salient and small lesions (e.g., microaneurysms on fundus) both play significant roles in real-world disease diagnosis under medical image examinations. Although deep neural networks (DNNs) have achieved promising medical image classification perform...

[Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].

Die Ophthalmologie
Imaging disciplines, such as ophthalmology, offer a wide range of opportunities for the beneficial use of artificial intelligence (AI). The analysis of images and data by trained algorithms has the potential to facilitate making the diagnosis and pat...

A segment anything model-guided and match-based semi-supervised segmentation framework for medical imaging.

Medical physics
BACKGROUND: Semi-supervised segmentation leverages sparse annotation information to learn rich representations from combined labeled and label-less data for segmentation tasks. The Match-based framework, by using the consistency constraint of segment...

Dual-Filter Cross Attention and Onion Pooling Network for Enhanced Few-Shot Medical Image Segmentation.

Sensors (Basel, Switzerland)
Few-shot learning has demonstrated remarkable performance in medical image segmentation. However, existing few-shot medical image segmentation (FSMIS) models often struggle to fully utilize query image information, leading to prototype bias and limit...

Demographic bias of expert-level vision-language foundation models in medical imaging.

Science advances
Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit trainin...

Multimodal generative AI for medical image interpretation.

Nature
Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as mul...